Dealing with uncertainty in landscape genetic resistance models: a case of three co‐occurring marsupials

Abstract

Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small data sets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multimodel inference as an improvement over single model-based inference. We illustrate the approach empirically using co-occurring, woodland-preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground-dwelling antechinus (Antechinus flavipes). First, we use maximum-likelihood and a bootstrap procedure to identify the best-supported isolation-by-resistance model out of 56 models defined by linear and non-linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small data sets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision-making, where dealing with uncertainty is critical.

Conventional methods for enhancing connectivity in conservation planning do not always maintain gene flow

“…To validate the resistance maps, we calculated R 2 β statistics and confirmed that they explained an adequate proportion of the sampled genetic variation for each species (mean 0.63 R 2 β ± 0.13 SD ; Supporting Information Table ). We also verified that these statistics are robust to uncertainty using bootstrap analyses (10,000 replicates with 80% of the data; Supporting Information Table ; following Dudaniec et al., ). As mentioned earlier, the analysis originally included one additional species ( G. nivalis ) but it was omitted because its resistance maps had poor predictive abilities ( R 2 β ≤ 0.35; following Keller et al., ).…”

Detection of environmental and morphological adaptation despite high landscape genetic connectivity in a pest grasshopper (Phaulacridium vittatum)

“…We generated multiple resistance surfaces from our parameterized Annual Temp and Land Cover data to test for multiple hypotheses about their effects on genetic distance. To generate alternative resistance surfaces with varying resistance, we applied a similar method as implemented by Dudaniec et al (, ), which is a modified approach of Shirk et al (), whereby different values of intercept ( α ) and slope ( γ ) parameters are used to create linear and nonlinear resistance surfaces. We applied the following formulas to generate resistance relationships:where r i is the resistance of cell i , T i is the Annual Temp of cell i (Equation ), L i is the rank of Land Cover type of cell i (Equation ) and max is the maximum value of the raster surface.…”

Comparative landscape genetics of pond‐breeding amphibians in Mediterranean temporal wetlands: The positive role of structural heterogeneity in promoting gene flow

“…Comparative landscape genetics studies on codistributed species have great potential to design cost‐effective conservation plans focusing on measures favouring a wider set of taxa, but are still relatively scarce. So far, comparative studies have focused primarily on vertebrates, including amphibians (Coster, Babbitt, Cooper, & Kovach, ; Richardson, ; Zancolli, Rödel, Steffan‐Dewenter, & Storfer, ), mammals (Dudaniec et al., ; Frantz et al., ; Muscarella, Murray, Ortt, Russell, & Fleming, ), and fishes (Olsen et al., ) and more occasionally on invertebrates (Engler, Balkenhol, Filz, Habel, & Rödder, ; Ortego, García‐Navas, Noguerales, & Cordero, ; Phillipsen et al., ). These multi‐species studies may allow identifying interspecific differences in the way landscape features influence connectivity and gene flow and provide general guidelines for land management programmes aimed at protecting biological communities or ecosystems (Goldberg & Waits, ; Keller, Holderegger, Strien, & Bolliger, ; Nicholson & Possingham, ; Schwenk & Donovan, ).…”